# Note: if you intend to run this script make sure you look under scripts/fsmt/
# to locate the appropriate script to do the work correctly. There is a set of scripts to:
# - download and prepare data and run the conversion script
# - perform eval to get the best hparam into the config
# - generate model_cards - useful if you have multiple models from the same paper

import argparse
import json
import os
import re
from collections import OrderedDict
from os.path import basename, dirname

import fairseq
import torch
from fairseq import hub_utils
from fairseq.data.dictionary import Dictionary

from transformers import FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils import WEIGHTS_NAME, logging


logging.set_verbosity_warning()

A						=			2

# based on the results of a search on a range of `num_beams`, `length_penalty` and `early_stopping`
# values against wmt19 test data to obtain the best BLEU scores, we will use the following defaults:
#
# * `num_beams`: 5 (higher scores better, but requires more memory/is slower, can be adjusted by users)
# * `early_stopping`: `False` consistently scored better
# * `length_penalty` varied, so will assign the best one depending on the model
A						=			{
    # fairseq:
    'wmt19-ru-en': {'length_penalty': 1.1},
    'wmt19-en-ru': {'length_penalty': 1.15},
    'wmt19-en-de': {'length_penalty': 1.0},
    'wmt19-de-en': {'length_penalty': 1.1},
    # allenai:
    'wmt16-en-de-dist-12-1': {'length_penalty': 0.6},
    'wmt16-en-de-dist-6-1': {'length_penalty': 0.6},
    'wmt16-en-de-12-1': {'length_penalty': 0.8},
    'wmt19-de-en-6-6-base': {'length_penalty': 0.6},
    'wmt19-de-en-6-6-big': {'length_penalty': 0.6},
}

# this remaps the different models to their organization names
A						=			{}
for m in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
				A						=			'facebook'
for m in [
    "wmt16-en-de-dist-12-1",
    "wmt16-en-de-dist-6-1",
    "wmt16-en-de-12-1",
    "wmt19-de-en-6-6-base",
    "wmt19-de-en-6-6-big",
]:
				A						=			'allenai'


def 	lowerCamelCase					(	UpperCamelCase     :				Optional[Any]							)   ->    Tuple:
		# (1) remove word breaking symbol, (2) add word ending symbol where the word is not broken up,
		# e.g.: d = {'le@@': 5, 'tt@@': 6, 'er': 7} => {'le': 5, 'tt': 6, 'er</w>': 7}
		_lowerCamelCase				   =  dict((re.sub(R'@@$'			,       ''			,       UpperCamelCase							), v) if k.endswith('@@'							) else (re.sub(R'$'			,       '</w>'			,       UpperCamelCase							), v) for k, v in d.items()							)
		_lowerCamelCase				   =  '<s> <pad> </s> <unk>'.split()
		# restore the special tokens
		for k in keep_keys:
				del da[F"""{k}</w>"""]
				_lowerCamelCase				   =  d[k]  # restore
		return da


def 	lowerCamelCase					(	UpperCamelCase     :				str			,       UpperCamelCase     :				Optional[Any]							)   ->    Dict:
		# prep
		assert os.path.exists(UpperCamelCase							)
		os.makedirs(UpperCamelCase			,       exist_ok=UpperCamelCase							)
		print(F"""Writing results to {pytorch_dump_folder_path}"""							)

		# handle various types of models

		_lowerCamelCase				   =  basename(UpperCamelCase							)
		_lowerCamelCase				   =  dirname(UpperCamelCase							)

		_lowerCamelCase				   =  fairseq.model_parallel.models.transformer.ModelParallelTransformerModel
		_lowerCamelCase				   =  cls.hub_models()
		_lowerCamelCase				   =  {'bpe': 'fastbpe', 'tokenizer': 'moses'}
		_lowerCamelCase				   =  '.'
		# note: since the model dump is old, fairseq has upgraded its model some
		# time later, and it does a whole lot of rewrites and splits on the saved
		# weights, therefore we can't use torch.load() directly on the model file.
		# see: upgrade_state_dict(state_dict) in fairseq_model.py
		print(F"""using checkpoint {checkpoint_file}"""							)
		_lowerCamelCase				   =  hub_utils.from_pretrained(
		    UpperCamelCase			,       UpperCamelCase			,       UpperCamelCase			,       archive_map=UpperCamelCase			,       **UpperCamelCase							)

		_lowerCamelCase				   =  vars(chkpt['args']['model']							)

		_lowerCamelCase				   =  args['source_lang']
		_lowerCamelCase				   =  args['target_lang']

		_lowerCamelCase				   =  dirname(UpperCamelCase							)
		_lowerCamelCase				   =  basename(UpperCamelCase							)

		# dicts
		_lowerCamelCase				   =  os.path.join(UpperCamelCase			,       F"""dict.{src_lang}.txt"""							)
		_lowerCamelCase				   =  os.path.join(UpperCamelCase			,       F"""dict.{tgt_lang}.txt"""							)

		_lowerCamelCase				   =  Dictionary.load(UpperCamelCase							)
		_lowerCamelCase				   =  rewrite_dict_keys(src_dict.indices							)
		_lowerCamelCase				   =  len(UpperCamelCase							)
		_lowerCamelCase				   =  os.path.join(UpperCamelCase			,       'vocab-src.json'							)
		print(F"""Generating {src_vocab_file} of {src_vocab_size} of {src_lang} records"""							)
		with open(UpperCamelCase			,       'w'			,       encoding='utf-8'							) as f:
				f.write(json.dumps(UpperCamelCase			,       ensure_ascii=UpperCamelCase			,       indent=UpperCamelCase							)							)

		# detect whether this is a do_lower_case situation, which can be derived by checking whether we
		# have at least one uppercase letter in the source vocab
		_lowerCamelCase				   =  True
		for k in src_vocab.keys():
				if not k.islower():
						_lowerCamelCase				   =  False
						break

		_lowerCamelCase				   =  Dictionary.load(UpperCamelCase							)
		_lowerCamelCase				   =  rewrite_dict_keys(tgt_dict.indices							)
		_lowerCamelCase				   =  len(UpperCamelCase							)
		_lowerCamelCase				   =  os.path.join(UpperCamelCase			,       'vocab-tgt.json'							)
		print(F"""Generating {tgt_vocab_file} of {tgt_vocab_size} of {tgt_lang} records"""							)
		with open(UpperCamelCase			,       'w'			,       encoding='utf-8'							) as f:
				f.write(json.dumps(UpperCamelCase			,       ensure_ascii=UpperCamelCase			,       indent=UpperCamelCase							)							)

		# merges_file (bpecodes)
		_lowerCamelCase				   =  os.path.join(UpperCamelCase			,       VOCAB_FILES_NAMES['merges_file']							)
		for fn in ["bpecodes", "code"]:  # older fairseq called the merges file "code"
				_lowerCamelCase				   =  os.path.join(UpperCamelCase			,       UpperCamelCase							)
				if os.path.exists(UpperCamelCase							):
						break
		with open(UpperCamelCase			,       encoding='utf-8'							) as fin:
				_lowerCamelCase				   =  fin.read()
		_lowerCamelCase				   =  re.sub(R' \d+$'			,       ''			,       UpperCamelCase			,       0			,       re.M							)  # remove frequency number
		print(F"""Generating {merges_file}"""							)
		with open(UpperCamelCase			,       'w'			,       encoding='utf-8'							) as fout:
				fout.write(UpperCamelCase							)

		# model config
		_lowerCamelCase				   =  os.path.join(UpperCamelCase			,       'config.json'							)

		# validate bpe/tokenizer config, as currently it's hardcoded to moses+fastbpe -
		# may have to modify the tokenizer if a different type is used by a future model
		assert args["bpe"] == "fastbpe", F"""need to extend tokenizer to support bpe={args["bpe"]}"""
		assert args["tokenizer"] == "moses", F"""need to extend tokenizer to support bpe={args["tokenizer"]}"""

		_lowerCamelCase				   =  {
		    'architectures': ['FSMTForConditionalGeneration'],
		    'model_type': 'fsmt',
		    'activation_dropout': args['activation_dropout'],
		    'activation_function': 'relu',
		    'attention_dropout': args['attention_dropout'],
		    'd_model': args['decoder_embed_dim'],
		    'dropout': args['dropout'],
		    'init_std': 0.02,
		    'max_position_embeddings': args['max_source_positions'],
		    'num_hidden_layers': args['encoder_layers'],
		    'src_vocab_size': src_vocab_size,
		    'tgt_vocab_size': tgt_vocab_size,
		    'langs': [src_lang, tgt_lang],
		    'encoder_attention_heads': args['encoder_attention_heads'],
		    'encoder_ffn_dim': args['encoder_ffn_embed_dim'],
		    'encoder_layerdrop': args['encoder_layerdrop'],
		    'encoder_layers': args['encoder_layers'],
		    'decoder_attention_heads': args['decoder_attention_heads'],
		    'decoder_ffn_dim': args['decoder_ffn_embed_dim'],
		    'decoder_layerdrop': args['decoder_layerdrop'],
		    'decoder_layers': args['decoder_layers'],
		    'bos_token_id': 0,
		    'pad_token_id': 1,
		    'eos_token_id': 2,
		    'is_encoder_decoder': True,
		    'scale_embedding': not args['no_scale_embedding'],
		    'tie_word_embeddings': args['share_all_embeddings'],
		}

		# good hparam defaults to start with
		_lowerCamelCase				   =  5
		_lowerCamelCase				   =  False
		if model_dir in best_score_hparams and "length_penalty" in best_score_hparams[model_dir]:
				_lowerCamelCase				   =  best_score_hparams[model_dir]['length_penalty']
		else:
				_lowerCamelCase				   =  1.0

		print(F"""Generating {fsmt_model_config_file}"""							)
		with open(UpperCamelCase			,       'w'			,       encoding='utf-8'							) as f:
				f.write(json.dumps(UpperCamelCase			,       ensure_ascii=UpperCamelCase			,       indent=UpperCamelCase							)							)

		# tokenizer config
		_lowerCamelCase				   =  os.path.join(UpperCamelCase			,       UpperCamelCase							)

		_lowerCamelCase				   =  {
		    'langs': [src_lang, tgt_lang],
		    'model_max_length': 10_24,
		    'do_lower_case': do_lower_case,
		}

		print(F"""Generating {fsmt_tokenizer_config_file}"""							)
		with open(UpperCamelCase			,       'w'			,       encoding='utf-8'							) as f:
				f.write(json.dumps(UpperCamelCase			,       ensure_ascii=UpperCamelCase			,       indent=UpperCamelCase							)							)

		# model
		_lowerCamelCase				   =  chkpt['models'][0]
		_lowerCamelCase				   =  model.state_dict()

		# rename keys to start with 'model.'
		_lowerCamelCase				   =  OrderedDict(('model.' + k, v) for k, v in model_state_dict.items()							)

		# remove unneeded keys
		_lowerCamelCase				   =  [
		    'model.model',
		    'model.encoder.version',
		    'model.decoder.version',
		    'model.encoder_embed_tokens.weight',
		    'model.decoder_embed_tokens.weight',
		    'model.encoder.embed_positions._float_tensor',
		    'model.decoder.embed_positions._float_tensor',
		]
		for k in ignore_keys:
				model_state_dict.pop(UpperCamelCase			,       UpperCamelCase							)

		_lowerCamelCase				   =  FSMTConfig.from_pretrained(UpperCamelCase							)
		_lowerCamelCase				   =  FSMTForConditionalGeneration(UpperCamelCase							)

		# check that it loads ok
		model_new.load_state_dict(UpperCamelCase			,       strict=UpperCamelCase							)

		# save
		_lowerCamelCase				   =  os.path.join(UpperCamelCase			,       UpperCamelCase							)
		print(F"""Generating {pytorch_weights_dump_path}"""							)
		torch.save(UpperCamelCase			,       UpperCamelCase							)

		print('Conversion is done!'							)
		print('\nLast step is to upload the files to s3'							)
		print(F"""cd {data_root}"""							)
		print(F"""transformers-cli upload {model_dir}"""							)


if __name__ == "__main__":
				A						=			argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    '--fsmt_checkpoint_path',
				    default=None,
				    type=str,
				    required=True,
				    help=(
				        'Path to the official PyTorch checkpoint file which is expected to reside in the dump dir with dicts,'
				        ' bpecodes, etc.'
				    ),
				)
				parser.add_argument(
				    '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
				)
				A						=			parser.parse_args()
				convert_fsmt_checkpoint_to_pytorch(args.fsmt_checkpoint_path, args.pytorch_dump_folder_path)
import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel

from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import floats_tensor, load_image, load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference





class     lowerCAmelCase__     (				SCREAMING_SNAKE_CASE_     ,unittest.TestCase			):


		'''simple docstring'''






		lowerCAmelCase_     =   ShapEImgaImgPipeline
		lowerCAmelCase_     =   ['image']
		lowerCAmelCase_     =   ['image']
		lowerCAmelCase_     =   [
		    'num_images_per_prompt',
		    'num_inference_steps',
		    'generator',
		    'latents',
		    'guidance_scale',
		    'frame_size',
		    'output_type',
		    'return_dict',
		]
		lowerCAmelCase_     =   False
		@property
		def   _snake_case       (       self     :     List[str]		)      ->	Any:
				return 3_2
		@property
		def   _snake_case       (       self     :     int		)      ->	Dict:
				return 3_2
		@property
		def   _snake_case       (       self     :     Tuple		)      ->	Optional[Any]:
				return self.time_input_dim * 4
		@property
		def   _snake_case       (       self     :     Tuple		)      ->	Optional[Any]:
				return 8
		@property
		def   _snake_case       (       self     :     int		)      ->	Dict:
				torch.manual_seed(0		)
				_lowerCamelCase				   =  CLIPVisionConfig(
				    hidden_size=self.text_embedder_hidden_size     ,  image_size=6_4     ,  projection_dim=self.text_embedder_hidden_size     ,  intermediate_size=3_7     ,  num_attention_heads=4     ,  num_channels=3     ,  num_hidden_layers=5     ,  patch_size=1     ,  )

				_lowerCamelCase				   =  CLIPVisionModel(snake_case__		)
				return model
		@property
		def   _snake_case       (       self     :     Tuple		)      ->	Any:
				_lowerCamelCase				   =  CLIPImageProcessor(
				    crop_size=2_2_4     ,  do_center_crop=snake_case__     ,  do_normalize=snake_case__     ,  do_resize=snake_case__     ,  image_mean=[0.4814_5466, 0.457_8275, 0.4082_1073]     ,  image_std=[0.2686_2954, 0.2613_0258, 0.2757_7711]     ,  resample=3     ,  size=2_2_4     ,  )

				return image_processor
		@property
		def   _snake_case       (       self     :     List[str]		)      ->	Any:
				torch.manual_seed(0		)

				_lowerCamelCase				   =  {
				    'num_attention_heads': 2,
				    'attention_head_dim': 1_6,
				    'embedding_dim': self.time_input_dim,
				    'num_embeddings': 3_2,
				    'embedding_proj_dim': self.text_embedder_hidden_size,
				    'time_embed_dim': self.time_embed_dim,
				    'num_layers': 1,
				    'clip_embed_dim': self.time_input_dim * 2,
				    'additional_embeddings': 0,
				    'time_embed_act_fn': 'gelu',
				    'norm_in_type': 'layer',
				    'embedding_proj_norm_type': 'layer',
				    'encoder_hid_proj_type': None,
				    'added_emb_type': None,
				}

				_lowerCamelCase				   =  PriorTransformer(**snake_case__		)
				return model
		@property
		def   _snake_case       (       self     :     List[Any]		)      ->	Optional[int]:
				torch.manual_seed(0		)

				_lowerCamelCase				   =  {
				    'param_shapes': (
				        (self.renderer_dim, 9_3),
				        (self.renderer_dim, 8),
				        (self.renderer_dim, 8),
				        (self.renderer_dim, 8),
				    ),
				    'd_latent': self.time_input_dim,
				    'd_hidden': self.renderer_dim,
				    'n_output': 1_2,
				    'background': (
				        0.1,
				        0.1,
				        0.1,
				    ),
				}
				_lowerCamelCase				   =  ShapERenderer(**snake_case__		)
				return model
		def   _snake_case       (       self     :     Any		)      ->	str:
				_lowerCamelCase				   =  self.dummy_prior
				_lowerCamelCase				   =  self.dummy_image_encoder
				_lowerCamelCase				   =  self.dummy_image_processor
				_lowerCamelCase				   =  self.dummy_renderer

				_lowerCamelCase				   =  HeunDiscreteScheduler(
				    beta_schedule='exp'     ,  num_train_timesteps=1_0_2_4     ,  prediction_type='sample'     ,  use_karras_sigmas=snake_case__     ,  clip_sample=snake_case__     ,  clip_sample_range=1.0     ,  )
				_lowerCamelCase				   =  {
				    'prior': prior,
				    'image_encoder': image_encoder,
				    'image_processor': image_processor,
				    'renderer': renderer,
				    'scheduler': scheduler,
				}

				return components
		def   _snake_case       (       self     :     List[str]     ,  snake_case__     :     str     ,  snake_case__     :     Optional[Any]=0		)      ->	Union[str, Any]:
				_lowerCamelCase				   =  floats_tensor((1, 3, 6_4, 6_4)     ,  rng=random.Random(snake_case__		)		).to(snake_case__		)

				if str(snake_case__		).startswith('mps'		):
						_lowerCamelCase				   =  torch.manual_seed(snake_case__		)
				else:
						_lowerCamelCase				   =  torch.Generator(device=snake_case__		).manual_seed(snake_case__		)
				_lowerCamelCase				   =  {
				    'image': input_image,
				    'generator': generator,
				    'num_inference_steps': 1,
				    'frame_size': 3_2,
				    'output_type': 'np',
				}
				return inputs
		def   _snake_case       (       self     :     Union[str, Any]		)      ->	Union[str, Any]:
				_lowerCamelCase				   =  'cpu'

				_lowerCamelCase				   =  self.get_dummy_components()

				_lowerCamelCase				   =  self.pipeline_class(**snake_case__		)
				_lowerCamelCase				   =  pipe.to(snake_case__		)

				pipe.set_progress_bar_config(disable=snake_case__		)

				_lowerCamelCase				   =  pipe(**self.get_dummy_inputs(snake_case__		)		)
				_lowerCamelCase				   =  output.images[0]
				_lowerCamelCase				   =  image[0, -3:, -3:, -1]

				assert image.shape == (2_0, 3_2, 3_2, 3)

				_lowerCamelCase				   =  np.array(
				    [
				        0.0003_9216,
				        0.0003_9216,
				        0.0003_9216,
				        0.0003_9216,
				        0.0003_9216,
				        0.0003_9216,
				        0.0003_9216,
				        0.0003_9216,
				        0.0003_9216,
				    ]		)

				assert np.abs(image_slice.flatten() - expected_slice		).max() < 1e-2
		def   _snake_case       (       self     :     Dict		)      ->	Optional[Any]:
				# NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
				self._test_inference_batch_consistent(batch_sizes=[1, 2]		)
		def   _snake_case       (       self     :     Optional[Any]		)      ->	Tuple:
				_lowerCamelCase				   =  torch_device == 'cpu'
				_lowerCamelCase				   =  True
				self._test_inference_batch_single_identical(
				    batch_size=2     ,  test_max_difference=snake_case__     ,  relax_max_difference=snake_case__     ,  )



		def   _snake_case       (       self     :     Any		)      ->	Tuple:
				_lowerCamelCase				   =  self.get_dummy_components()
				_lowerCamelCase				   =  self.pipeline_class(**snake_case__		)
				_lowerCamelCase				   =  pipe.to(snake_case__		)
				pipe.set_progress_bar_config(disable=snake_case__		)

				_lowerCamelCase				   =  1
				_lowerCamelCase				   =  2

				_lowerCamelCase				   =  self.get_dummy_inputs(snake_case__		)

				for key in inputs.keys():
						if key in self.batch_params:
								_lowerCamelCase				   =  batch_size * [inputs[key]]

				_lowerCamelCase				   =  pipe(**snake_case__     ,  num_images_per_prompt=snake_case__		)[0]

				assert images.shape[0] == batch_size * num_images_per_prompt





@slow
@require_torch_gpu
class     lowerCAmelCase__     (				unittest.TestCase			):


		'''simple docstring'''






		def   _snake_case       (       self     :     Union[str, Any]		)      ->	Optional[int]:
				# clean up the VRAM after each test
				super().tearDown()
				gc.collect()
				torch.cuda.empty_cache()



		def   _snake_case       (       self     :     List[str]		)      ->	List[Any]:
				_lowerCamelCase				   =  load_image(
				    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/shap_e/corgi.png'		)
				_lowerCamelCase				   =  load_numpy(
				    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
				    '/shap_e/test_shap_e_img2img_out.npy'		)
				_lowerCamelCase				   =  ShapEImgaImgPipeline.from_pretrained('openai/shap-e-img2img'		)
				_lowerCamelCase				   =  pipe.to(snake_case__		)
				pipe.set_progress_bar_config(disable=snake_case__		)

				_lowerCamelCase				   =  torch.Generator(device=snake_case__		).manual_seed(0		)

				_lowerCamelCase				   =  pipe(
				    snake_case__     ,  generator=snake_case__     ,  guidance_scale=3.0     ,  num_inference_steps=6_4     ,  frame_size=6_4     ,  output_type='np'     ,  ).images[0]

				assert images.shape == (2_0, 6_4, 6_4, 3)

				assert_mean_pixel_difference(snake_case__     ,  snake_case__		)